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Fixed database typo and removed unnecessary class identifier.

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Batuhan Berk Başoğlu 2020-10-14 10:10:37 -04:00
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venv/Lib/site-packages/skimage/measure/_find_contours.py Normal file
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import numpy as np
from ._find_contours_cy import _get_contour_segments
from collections import deque
_param_options = ('high', 'low')
def find_contours(array, level,
fully_connected='low', positive_orientation='low',
*,
mask=None):
"""Find iso-valued contours in a 2D array for a given level value.
Uses the "marching squares" method to compute a the iso-valued contours of
the input 2D array for a particular level value. Array values are linearly
interpolated to provide better precision for the output contours.
Parameters
----------
array : 2D ndarray of double
Input data in which to find contours.
level : float
Value along which to find contours in the array.
fully_connected : str, {'low', 'high'}
Indicates whether array elements below the given level value are to be
considered fully-connected (and hence elements above the value will
only be face connected), or vice-versa. (See notes below for details.)
positive_orientation : either 'low' or 'high'
Indicates whether the output contours will produce positively-oriented
polygons around islands of low- or high-valued elements. If 'low' then
contours will wind counter- clockwise around elements below the
iso-value. Alternately, this means that low-valued elements are always
on the left of the contour. (See below for details.)
mask : 2D ndarray of bool, or None
A boolean mask, True where we want to draw contours.
Note that NaN values are always excluded from the considered region
(``mask`` is set to ``False`` wherever ``array`` is ``NaN``).
Returns
-------
contours : list of (n,2)-ndarrays
Each contour is an ndarray of shape ``(n, 2)``,
consisting of n ``(row, column)`` coordinates along the contour.
Notes
-----
The marching squares algorithm is a special case of the marching cubes
algorithm [1]_. A simple explanation is available here::
http://users.polytech.unice.fr/~lingrand/MarchingCubes/algo.html
There is a single ambiguous case in the marching squares algorithm: when
a given ``2 x 2``-element square has two high-valued and two low-valued
elements, each pair diagonally adjacent. (Where high- and low-valued is
with respect to the contour value sought.) In this case, either the
high-valued elements can be 'connected together' via a thin isthmus that
separates the low-valued elements, or vice-versa. When elements are
connected together across a diagonal, they are considered 'fully
connected' (also known as 'face+vertex-connected' or '8-connected'). Only
high-valued or low-valued elements can be fully-connected, the other set
will be considered as 'face-connected' or '4-connected'. By default,
low-valued elements are considered fully-connected; this can be altered
with the 'fully_connected' parameter.
Output contours are not guaranteed to be closed: contours which intersect
the array edge or a masked-off region (either where mask is False or where
array is NaN) will be left open. All other contours will be closed. (The
closed-ness of a contours can be tested by checking whether the beginning
point is the same as the end point.)
Contours are oriented. By default, array values lower than the contour
value are to the left of the contour and values greater than the contour
value are to the right. This means that contours will wind
counter-clockwise (i.e. in 'positive orientation') around islands of
low-valued pixels. This behavior can be altered with the
'positive_orientation' parameter.
The order of the contours in the output list is determined by the position
of the smallest ``x,y`` (in lexicographical order) coordinate in the
contour. This is a side-effect of how the input array is traversed, but
can be relied upon.
.. warning::
Array coordinates/values are assumed to refer to the *center* of the
array element. Take a simple example input: ``[0, 1]``. The interpolated
position of 0.5 in this array is midway between the 0-element (at
``x=0``) and the 1-element (at ``x=1``), and thus would fall at
``x=0.5``.
This means that to find reasonable contours, it is best to find contours
midway between the expected "light" and "dark" values. In particular,
given a binarized array, *do not* choose to find contours at the low or
high value of the array. This will often yield degenerate contours,
especially around structures that are a single array element wide. Instead
choose a middle value, as above.
References
----------
.. [1] Lorensen, William and Harvey E. Cline. Marching Cubes: A High
Resolution 3D Surface Construction Algorithm. Computer Graphics
(SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170).
Examples
--------
>>> a = np.zeros((3, 3))
>>> a[0, 0] = 1
>>> a
array([[1., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
>>> find_contours(a, 0.5)
[array([[0. , 0.5],
[0.5, 0. ]])]
"""
if fully_connected not in _param_options:
raise ValueError('Parameters "fully_connected" must be either '
'"high" or "low".')
if positive_orientation not in _param_options:
raise ValueError('Parameters "positive_orientation" must be either '
'"high" or "low".')
if array.shape[0] < 2 or array.shape[1] < 2:
raise ValueError("Input array must be at least 2x2.")
if array.ndim != 2:
raise ValueError('Only 2D arrays are supported.')
if mask is not None:
if mask.shape != array.shape:
raise ValueError('Parameters "array" and "mask"'
' must have same shape.')
if not np.can_cast(mask.dtype, bool, casting='safe'):
raise TypeError('Parameter "mask" must be a binary array.')
mask = mask.astype(np.uint8, copy=False)
segments = _get_contour_segments(array.astype(np.double), float(level),
fully_connected == 'high', mask=mask)
contours = _assemble_contours(segments)
if positive_orientation == 'high':
contours = [c[::-1] for c in contours]
return contours
def _assemble_contours(segments):
current_index = 0
contours = {}
starts = {}
ends = {}
for from_point, to_point in segments:
# Ignore degenerate segments.
# This happens when (and only when) one vertex of the square is
# exactly the contour level, and the rest are above or below.
# This degenerate vertex will be picked up later by neighboring
# squares.
if from_point == to_point:
continue
tail, tail_num = starts.pop(to_point, (None, None))
head, head_num = ends.pop(from_point, (None, None))
if tail is not None and head is not None:
# We need to connect these two contours.
if tail is head:
# We need to closed a contour.
# Add the end point
head.append(to_point)
else: # tail is not head
# We need to join two distinct contours.
# We want to keep the first contour segment created, so that
# the final contours are ordered left->right, top->bottom.
if tail_num > head_num:
# tail was created second. Append tail to head.
head.extend(tail)
# remove all traces of tail:
ends.pop(tail[-1])
contours.pop(tail_num, None)
# Update contour starts end ends
starts[head[0]] = (head, head_num)
ends[head[-1]] = (head, head_num)
else: # tail_num <= head_num
# head was created second. Prepend head to tail.
tail.extendleft(reversed(head))
# remove all traces of head:
starts.pop(head[0])
contours.pop(head_num, None)
# Update contour starts end ends
starts[tail[0]] = (tail, tail_num)
ends[tail[-1]] = (tail, tail_num)
elif tail is None and head is None:
# we need to add a new contour
new_contour = deque((from_point, to_point))
contours[current_index] = new_contour
starts[from_point] = (new_contour, current_index)
ends[to_point] = (new_contour, current_index)
current_index += 1
elif head is None: # tail is not None
# We've found a single contour to which the new segment should be
# prepended.
tail.appendleft(from_point)
starts[from_point] = (tail, tail_num)
else: # tail is None and head is not None:
# We've found a single contour to which the new segment should be
# appended
head.append(to_point)
ends[to_point] = (head, head_num)
return [np.array(contour) for _, contour in sorted(contours.items())]